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Spring 2018 Computational Methods — Final Projects

The goal of Computational Methods in Psychology and Neuroscience is to acquaint students with scientific computing, broadly speaking, but especially as it applies to psychology and neuroscience.

Even so, it attracts students from a pretty wide swath of majors. This year, in addition to psychology and neuroscience, we had majors from business, biology, as well as political and computer sciences.

Over the years we have used a variety of software in the course including Python, Matlab and Mathematica, as well as purpose-built environments like PsychoPy, freesurfer, ImageJ and others.

This year, we focused on Mathematica as it provides a rich set of tools and access to data and datasets without the sometimes painful management of packages and such1.

Final Projects

This year, the final projects were self-determined. Individuals and teams pitched their proposals early in the semester and we refined and implemented them throughout the rest of the term. They then pitched the final work and demonstrated what they had accomplished (and failed to).

Some of these projects are super ambitious for an introductory class, but the goal was learning and understanding the problem solving needed. Not so much minute implementation and theoretical details. Even if the problem wasn’t ‘solved’ in every case I feel like each individual / group now has a much better sense of what is possible and what is difficult2. In some cases, I implemented ‘helper’ code that is now part of the FPTools repository, but the ideas and final implementations are their own.

A computational simulation of asteroid impact with the planet earth, featuring animations and mortality rates.

Kids and Words

Linguistic analysis of conversations between kids and their parents.

Morality in Political Candidates

In the wake of the Facebook/Cambridge Analytica fiesta, a look at some crowdsourced (MTurk) questionnaire data about the personalities of political candidates. Machine-learning modeled candidate preferences based on interactive input.

Cartoon Face Recognition

The predominant implementation of ‘face finding’ algorithms doesn’t do a very good job with cartoon faces. This machine learning project sets out to rectify this oversight.

This is especially true with young scientists just dipping their toes into scientific computing. Even with some of the great package and environment management software out there some scientific computing environments can be too much. ↩︎

On the first day of class, I type Sphere[]//Graphics3D into Mathematica and explain that, in 1983, I took 3 G/UG courses at OSU (CS 781–3) to get that to happen on a 320×240 pixel screen in roughly geological time. Then I shake my cane at them and tell them to get off my lawn. ↩︎